Qwen3.8 27B rotated W3 INT3 weights for the Paiton RDNA4 runtime

CALIBRATION.md

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+# Calibration data
+
+This file covers the calibration data of the INT3 weights in this repository (`w3rot-manifest.json`,
+SHA-256 `335c5c834fb3f455203908d2b5aff8131aca2fe64ad73d59feddf32247d41a70`). Every source carries a
+permissive license: Apache-2.0, MIT, BSD-3-Clause, ODC-By 1.0 or CC BY 4.0. The manifest's
+`sources.datasets` section records the revision, license and token count of all 33 entries.
+
+## Token counts
+
+- **Calibration:** 292,864 tokens (123 sequences of 2,048 tokens plus 5 long-context sequences of 8,192 tokens).
+- **Held-out:** 32,768 tokens (2 × 2,048 per category plus 1 × 8,192 long context), disjoint from the calibration rows.
+- **Category shares** follow the calibration mixture published for
+  [ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ](https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ).
+
+## Data mix
+
+| Category | Source | License | Tokens | Share |
+|---|---|---|---:|---:|
+| Math | [AI-MO/NuminaMath-CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | Apache-2.0 | 29,208 | 10.0 % |
+| Math | [EleutherAI/hendrycks_math](https://huggingface.co/datasets/EleutherAI/hendrycks_math), intermediate algebra ¹ | MIT | 9,704 | 3.3 % |
+| Code | [m-a-p/CodeFeedback-Filtered-Instruction](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) | Apache-2.0 | 29,118 | 9.9 % |
+| Code | Source files: Transformers (3), NumPy (5), PyTorch C++ headers (2) | Apache-2.0; BSD-3-Clause | 22,082 | 7.5 % |
+| Science | [HuggingFaceTB/cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia), `openstax` chemistry, biology and physics | Apache-2.0 | 45,119 | 15.4 % |
+| Science | [allenai/qasc](https://huggingface.co/datasets/allenai/qasc) | CC BY 4.0 | 14,273 | 4.9 % |
+| General | [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), `sample-10BT` | ODC-By 1.0 | 22,010 | 7.5 % |
+| General | [OpenAssistant/oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2), English first turns | Apache-2.0 | 14,854 | 5.1 % |
+| Multilingual | [HuggingFaceFW/fineweb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2), 18 languages ² | ODC-By 1.0 | 25,673 | 8.8 % |
+| Multilingual | [CohereLabs/aya_dataset](https://huggingface.co/datasets/CohereLabs/aya_dataset) | Apache-2.0 | 11,191 | 3.8 % |
+| Agentic | [SWE-Gym/OpenHands-SFT-Trajectories](https://huggingface.co/datasets/SWE-Gym/OpenHands-SFT-Trajectories) | MIT | 19,453 | 6.6 % |
+| Agentic | [NousResearch/hermes-function-calling-v1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1) | Apache-2.0 | 9,219 | 3.1 % |
+| Long context | HuggingFaceFW/fineweb-edu, documents over 40,000 characters | ODC-By 1.0 | 24,576 | 8.4 % |
+| Long context | [HuggingFaceFW/finepdfs-edu](https://huggingface.co/datasets/HuggingFaceFW/finepdfs-edu), English | ODC-By 1.0 | 16,384 | 5.6 % |
+| **Total** | | | **292,864** | **100 %** |
+
+CodeFeedback-Filtered-Instruction is Apache-2.0; its card notes that part of its content was generated with
+third-party language models.
+
+¹ The EleutherAI/hendrycks_math dataset card declares MIT. The original upload, `hendrycks/competition_math`,
+was disabled on the Hugging Face Hub after a DMCA notice. Its problems derive from AMC, AIME and Art of Problem
+Solving material, as do NuminaMath-CoT's `amc_aime`, `aops_forum` and `math` subsets.
+
+² Italian, Hindi, Spanish, Portuguese, German, Korean, Russian, Japanese, Indonesian, Chinese (Mandarin),
+Vietnamese, Turkish, Thai, Polish, Ukrainian, Dutch, French and Arabic.
+
+Rows were sampled at seeded random offsets. The data was used only to collect activation statistics for
+quantization, and it is not redistributed.
+
+## Decontamination
+
+We dropped every candidate row that shares a 13-word sequence (lower-cased) with GSM8K (train and test),
+HumanEval, MMLU-Pro (test and validation), our 38 chat evaluation prompts or the needle-test prompts. This
+removed 5 math rows. The packed sequences share no 13-word sequence with those sets. The manifest records hashes
+of the reference sets. A license check also excluded source files whose header named any other license.
+
+## Required attributions
+
+- **QASC.** Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen and Ashish Sabharwal, *QASC: A Dataset for
+  Question Answering via Sentence Composition* (Allen Institute for AI),
+  [allenai/qasc](https://huggingface.co/datasets/allenai/qasc). It is licensed under
+  [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) and provided as is. We reformatted its facts,
+  questions and answer options into calibration prompts, and no QASC text is redistributed.
+- **FineWeb-Edu, FineWeb-2 and FinePDFs-Edu.** The calibration contains information from
+  [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu),
+  [FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) and
+  [FinePDFs-Edu](https://huggingface.co/datasets/HuggingFaceFW/finepdfs-edu) (Hugging Face). They are made
+  available under the [ODC Attribution License 1.0](https://opendatacommons.org/licenses/by/1-0/), and their
+  use is also subject to [Common Crawl's Terms of Use](https://commoncrawl.org/terms-of-use).

LICENSE

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3.8-27B
+base_model_relation: quantized
+library_name: paiton
+pipeline_tag: text-generation
+inference: false
+tags: [paiton, vllm, amd, rocm, rdna4, gfx1201, int3, 3-bit, gptq, hadamard, w3a4, dflash2, speculative-decoding]
+---
+
+# Qwen3.8 27B rotated INT3 weights for Paiton on RDNA4
+
+These are 3-bit replacement weights for the decoder projections of [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B),
+calibrated with our own GPTQ run on permissively licensed data. They are an add-on to the
+[current Paiton Qwen3.8 release](https://huggingface.co/EliovpAI/Qwen3.8-27B-Quark-AWQ-MXFP4-DFlash2-Paiton-RDNA4) on one
+AMD Radeon AI PRO R9700, not a standalone checkpoint. The same target checkpoint supplies all other tensors, and the
+same DFlash2 drafter is used. Against the previous MXFP4 image, weighted decode rises **19.9 %**, prefill rises
+**5–13 %**, and model memory falls from 19.18 to 15.89 GiB. The cost is about 3 points of MMLU-Pro knowledge recall.
+
+**GPU:** Radeon AI PRO R9700 (`gfx1201`, 32 GB) · **Size:** 9.55 GB · **Context:** 65K profile · **Text only**
+· **Pre-release:** the items marked **PENDING** are filled in at release.
+
+## Requirements
+
+| Component | Requirement |
+|---|---|
+| GPU | One AMD Radeon AI PRO R9700 (`gfx1201`, RDNA4, 32 GB) |
+| Host | Linux x86-64, Python 3, Docker, AMD GPU device access (`/dev/kfd`, `/dev/dri`) |
+| Runtime image | `ghcr.io/eliovp/paiton-vllm-plugin:qwen38-rocm10-vllm029-65k-20260926-w3a4-r1` (digest **PENDING**) |
+| Launcher | `models/Qwen3.8-MXFP4-DFlash2/run-rocm10-65k.sh` in the [public plugin repository](https://github.com/Eliovp-BV/paiton-vllm-plugin) |
+| Target checkpoint | [`unsloth/Qwen3.8-27B-NVFP4`](https://huggingface.co/unsloth/Qwen3.8-27B-NVFP4/tree/f0b7c9e722f5565102fff8481c99e4d86ae099c7) at `f0b7c9e722f5565102fff8481c99e4d86ae099c7` |
+| Draft checkpoint | [`tcclaviger/Qwen3.8-27B-DFlash2-FP8`](https://huggingface.co/tcclaviger/Qwen3.8-27B-DFlash2-FP8/tree/ee0cb26a8279b7910cc28d82a8a3e15e4728d56f) at `ee0cb26a8279b7910cc28d82a8a3e15e4728d56f` |
+| These weights | This repository at `<REVISION>` (**PENDING**) |
+
+Use exactly these revisions. The rotated Gated DeltaNet b/a rows were derived from the target revision's bytes.
+
+## Quick start
+
+```bash
+git clone https://github.com/Eliovp-BV/paiton-vllm-plugin.git && cd paiton-vllm-plugin
+
+export PAITON_TARGET_DIR="$PWD/model-cache/qwen38-nvfp4"
+export PAITON_DRAFT_DIR="$PWD/model-cache/qwen38-dflash2"
+export PAITON_W3ROT_DIR="$PWD/model-cache/qwen38-w3rot-int3"
+export PAITON_CACHE_DIR="$PWD/runtime-cache/qwen38-rocm10-65k-w3a4"
+mkdir -p "$PAITON_TARGET_DIR" "$PAITON_DRAFT_DIR" "$PAITON_W3ROT_DIR" "$PAITON_CACHE_DIR"
+
+hf download unsloth/Qwen3.8-27B-NVFP4 \
+  --revision f0b7c9e722f5565102fff8481c99e4d86ae099c7 --local-dir "$PAITON_TARGET_DIR"
+hf download tcclaviger/Qwen3.8-27B-DFlash2-FP8 \
+  --revision ee0cb26a8279b7910cc28d82a8a3e15e4728d56f --local-dir "$PAITON_DRAFT_DIR"
+hf download EliovpAI/Qwen3.8-27B-W3Rot-INT3-Paiton-RDNA4 \
+  --revision <REVISION> --local-dir "$PAITON_W3ROT_DIR"
+(cd "$PAITON_W3ROT_DIR" && sha256sum -c SHA256SUMS)
+
+bash models/Qwen3.8-MXFP4-DFlash2/run-rocm10-65k.sh
+```
+
+When `PAITON_W3ROT_DIR` is set, the launcher serves these 3-bit weights automatically. `--weights mxfp4`
+forces the MXFP4 path on the same image. Once the server is ready, it accepts OpenAI-compatible requests at
+`http://127.0.0.1:18982/v1` with API model name `Qwen3.8`.
+
+## Method
+
+- **Weights.** The MLP, attention and Gated DeltaNet projections of all 64 layers (24.3 billion weights) are
+  symmetric INT3 with one BF16 scale per 128 input elements (3.125 bits per weight).
+- **Rotation.** MLP gate/up, attention q/k/v and Gated DeltaNet `in_proj` are stored in a block-diagonal
+  Hadamard-128 basis, and the runtime rotates their inputs to match.
+- **Calibration.** We ran sequential GPTQ in the rotated basis with an MSE scale search on 292,864 tokens of
+  permissively licensed data. [CALIBRATION.md](CALIBRATION.md) lists the sources.
+- **Prefill.** The rotated projections take 4-bit activations, scaled per token and per 128-element group.
+- **Everywhere else.** The other projections, and all projections during decode, take 8-bit (FP8) activations.
+- **Execution.** Native RDNA4 kernels in the Paiton image run these weights. All other tensors come from the
+  pinned target checkpoint.
+
+## Accuracy
+
+These results were measured in-model on 2026-09-26 with the Paiton image, using greedy decoding with thinking
+off. Each benchmark is paired against the MXFP4 image on identical items. Δ is in points with a
+normal-approximation 95 % CI, and p is the exact McNemar test.
+
+| Benchmark | MXFP4 | This model (W3A4) | Δ [95 % CI] | McNemar p |
+|---|---:|---:|---:|---:|
+| GSM8K 5-shot (1,319) | 95.68 % | 95.30 % | −0.38 [−1.44, +0.68] | 0.58 |
+| HumanEval pass@1 (164) | 95.12 % | 93.90 % | −1.22 [−4.99, +2.56] | 0.75 |
+| MMLU-Pro subset, 0-shot direct answer (14 × 100) | 62.57 % | 59.71 % | −2.86 [−4.81, −0.90] | 0.005 |
+| Needle at 61,440 tokens (80) | 100 % | 100 % | 0 | 1 |
+
+DFlash2 mean acceptance length relative to MXFP4 changes by +0.1 % on 38 chat prompts, −0.4 % on GSM8K,
++2.8 % on HumanEval, +1.8 % on MMLU-Pro and +2.0 % on the needle test.
+
+Math, code and long-context retrieval stay within noise. Knowledge recall drops by about 3 MMLU-Pro points,
+which is the trade-off of 3-bit weights. The MXFP4 path (`--weights mxfp4`) remains the choice for maximum
+knowledge accuracy.
+
+## Performance
+
+The benchmark is BetterBench 0.6.0 (quick preset) with the 65K release profile on one R9700, using two fresh
+server processes per image. The reference is the previous MXFP4 image, `qwen38-rocm10-vllm029-65k-20260924-r3`.
+These runs used the earlier v3 calibration of these weights. Speed does not depend on the calibration data,
+because the tensor format and runtime are identical. Changes are computed from unrounded values.
+
+| Metric | MXFP4 | These weights | Change |
+|---|---:|---:|---:|
+| **Weighted decode, tok/s** | **153.8** | **184.4** | **+19.9 %** |
+| chat / code / file edit | 121.2 / 179.9 / 179.9 | 135.8 / 226.0 / 195.0 | +12.0 / +25.6 / +8.5 % |
+| json / math / prose | 217.5 / 183.8 / 78.5 | 269.0 / 228.9 / 94.7 | +23.7 / +24.5 / +20.7 % |
+| reasoning / summarization | 117.8 / 138.4 | 133.5 / 158.4 | +13.3 / +14.4 % |
+| Aggregate at 1 / 2 / 4 / 8 requests, tok/s | 122.0 / 204.2 / 308.2 / 425.3 | 148.8 / 249.3 / 368.3 / 492.1 | +22.0 / +22.1 / +19.5 / +15.7 % |
+| Prefill at 2K / 8K / 16K, input tok/s | 3,689 / 3,834 / 3,871 | 4,156 / 4,165 / 4,103 | +12.7 / +8.6 / +6.0 % |
+| Prefill at 32K / 64K, input tok/s | 3,751 / 3,455 | 3,958 / 3,629 | +5.5 / +5.0 % |
+
+Time to first token shortens by the same factors as prefill throughput. Model memory falls from 19.18 GiB to
+15.89 GiB, and the launcher gives the freed VRAM to the KV cache: 250,578 cached tokens instead of 174,634, or 3.8
+concurrent 65K-token requests instead of 2.7.
+
+## Files and verification
+
+The repository holds 64 layer shards (`w3rot-L00.safetensors` … `w3rot-L63.safetensors`, 1,296 tensors),
+`w3rot-index.json`, `w3rot-manifest.json`, `CALIBRATION.md`, `LICENSE`, `THIRD_PARTY_NOTICES.md` and `SHA256SUMS`.
+The manifest records the format, per-shard SHA-256, b/a source hashes and calibration provenance, including 33
+dataset entries with revisions and licenses. The accuracy results above used this manifest, which is published
+unchanged. The paths in its `sources` section refer to the calibration environment.
+
+```bash
+sha256sum -c SHA256SUMS
+sha256sum w3rot-manifest.json   # 335c5c834fb3f455203908d2b5aff8131aca2fe64ad73d59feddf32247d41a70
+```
+
+## Limitations
+
+- The weights are lossy. MMLU-Pro scores about 3 points below MXFP4, outputs differ from the MXFP4 path, and
+  greedy generations can diverge.
+- The evaluation covers GSM8K, HumanEval, a 1,400-question MMLU-Pro subset, 38 chat prompts and needle retrieval
+  at 61,440 tokens. Multilingual, tool-calling and longer-context quality are not measured.
+- Only the 65K profile serves these weights; the 200K image uses MXFP4. The weights support text only and one
+  R9700 only, and Transformers, stock vLLM and llama.cpp cannot load them.
+- The weights are tied to the pinned target revision and to Paiton's rotation and activation formats.
+
+## License and attribution
+
+These weights are released under [Apache-2.0](LICENSE). They are a quantized derivative of
+[Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) (Apache-2.0), and the rotated b/a rows derive from
+[unsloth/Qwen3.8-27B-NVFP4](https://huggingface.co/unsloth/Qwen3.8-27B-NVFP4) (Apache-2.0). The method builds on
+GPTQ and [GSQ](https://github.com/IST-DASLab/GSQ) (Apache-2.0). The target checkpoint and the DFlash2 drafter
+([tcclaviger/Qwen3.8-27B-DFlash2-FP8](https://huggingface.co/tcclaviger/Qwen3.8-27B-DFlash2-FP8), Apache-2.0)
+are not redistributed here. The calibration data requires these attributions:
+
+- **QASC.** Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen and Ashish Sabharwal (Allen Institute for
+  AI), [allenai/qasc](https://huggingface.co/datasets/allenai/qasc). It is licensed under
+  [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) and provided as is. We reformatted it into
+  calibration prompts, and it is not redistributed.
+- **ODC-By datasets.** The calibration contains information from
+  [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu),
+  [FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) and
+  [FinePDFs-Edu](https://huggingface.co/datasets/HuggingFaceFW/finepdfs-edu) (Hugging Face). They are made
+  available under the [ODC Attribution License 1.0](https://opendatacommons.org/licenses/by/1-0/), and their
+  use is also subject to [Common Crawl's Terms of Use](https://commoncrawl.org/terms-of-use).
+
+[THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md) has the full notices ·
+[Paiton](https://eliovp.com/products/paiton) · [Public Paiton plugin](https://github.com/Eliovp-BV/paiton-vllm-plugin)

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THIRD_PARTY_NOTICES.md

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+# Third-party notices
+
+This repository distributes quantized weights derived from the models below. It
+contains no third-party code and no runtime artifacts.
+
+| Component | Upstream source and revision | License | Relationship |
+|---|---|---|---|
+| Base model | [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B/tree/1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0), `1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0` | Apache-2.0 | Source of the BF16 linear weights that were quantized. These weights are a quantized derivative. |
+| Target checkpoint | [unsloth/Qwen3.8-27B-NVFP4](https://huggingface.co/unsloth/Qwen3.8-27B-NVFP4/tree/f0b7c9e722f5565102fff8481c99e4d86ae099c7), `f0b7c9e722f5565102fff8481c99e4d86ae099c7` | Apache-2.0 | Source of the rotated Gated DeltaNet b/a rows and of the non-linear tensors used during calibration. Required at serving time and not redistributed. |
+| DFlash2 draft checkpoint | [tcclaviger/Qwen3.8-27B-DFlash2-FP8](https://huggingface.co/tcclaviger/Qwen3.8-27B-DFlash2-FP8/tree/ee0cb26a8279b7910cc28d82a8a3e15e4728d56f), `ee0cb26a8279b7910cc28d82a8a3e15e4728d56f`, derived from [z-lab/Qwen3.8-27B-DFlash2](https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2) (Apache-2.0) | Apache-2.0 | Required at serving time, used unchanged and not redistributed. |
+| GSQ | [IST-DASLab/GSQ](https://github.com/IST-DASLab/GSQ), [arXiv:2604.18556](https://arxiv.org/abs/2604.18556) | Apache-2.0 | We reimplemented its MSE scale search and use its tensor names. No GSQ code is included. |
+| GSQ 3-bit checkpoint | [ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ](https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ) | Apache-2.0 | Source of the calibration category mixture. None of its weights are included. |
+| GPTQ | Frantar et al., [arXiv:2210.17323](https://arxiv.org/abs/2210.17323) | Method | Quantization algorithm, reimplemented |
+| compressed-tensors | [vllm-project/compressed-tensors](https://github.com/vllm-project/compressed-tensors) | Apache-2.0 | 3-bit `pack_to_int32` storage layout. No code is included. |
+| Calibration data | 33 entries, listed in [CALIBRATION.md](CALIBRATION.md) | Apache-2.0, MIT, BSD-3-Clause, ODC-By 1.0, CC BY 4.0 | Used only to collect activation statistics. The data is not redistributed. |
+
+## Required attributions for the calibration data
+
+**QASC.** Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen and Ashish
+Sabharwal, *QASC: A Dataset for Question Answering via Sentence Composition*,
+Allen Institute for AI, [allenai/qasc](https://huggingface.co/datasets/allenai/qasc)
+(revision `a34ba204eb9a33b919c10cc08f4f1c8dae5ec070`). It is licensed under
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) and provided as is,
+without warranties. We reformatted its facts, questions and answer options into
+calibration prompts, and no QASC text is redistributed.
+
+**FineWeb-Edu, FineWeb-2 and FinePDFs-Edu.** This model's calibration contains
+information from the following datasets by Hugging Face. They are made available
+under the [ODC Attribution License 1.0](https://opendatacommons.org/licenses/by/1-0/),
+and their use is also subject to
+[Common Crawl's Terms of Use](https://commoncrawl.org/terms-of-use).
+
+- [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), revision `87f09149ef4734204d70ed1d046ddc9ca3f2b8f9`
+- [HuggingFaceFW/fineweb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2), revision `af9c13333eb981300149d5ca60a8e9d659b276b9`
+- [HuggingFaceFW/finepdfs-edu](https://huggingface.co/datasets/HuggingFaceFW/finepdfs-edu), revision `9cfabe2127faca99b3d5c4dc6d1fcb397399ebde`
+
+The Paiton runtime image that loads these weights is distributed separately.
+Upstream model, method and dataset terms remain those of their sources. The
+Apache-2.0 license text is in [LICENSE](LICENSE).

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